3D-imaging data acquired from a variety of platforms have become critical for ecological and environmental management. However, the use of disparate information sources to produce comprehensive and standardized global products is hindered by a lack of harmonization and terminology around ecosystem structure. We propose a sensor- and platform-independent framework which effectively distils the wealth of 3D information into concise ecosystem morphological traits - height, cover, and structural complexity - easy to conceptualize by ecologists and conservation stakeholders lacking remote sensing background. The conceptual disaggregation of ecosystem structure would contribute to defining and monitoring essential biodiversity variables obtained from 3D imaging that can be used to inform progress towards the UN 2030 Sustainable Development Goals and other international policy targets.